Retrieval-Augmented Generation (RAG) mitigates hallucinations in large language models by incorporating external knowledge. However, retrieval does not always return relevant documents and may return noisy ones. Indiscriminately retrieving and utilizing this external knowledge can interfere with the model’s originally correct reasoning. In this work, we propose Dual-Decision Retrieval-Augmented Generation (D²-RAG), which integrates multi-dimensional uncertainty estimation to decide whether to retrieve and employs adaptive contrastive decoding to handle retrieved contexts of varying quality. Specifically, we first integrate uncertainty estimation scores that assess model uncertainty from multiple perspectives, construct them into a comprehensive feature vector, and train a lightweight retrieval decision model to accurately identify the model’s knowledge boundaries and determine whether to retrieve. Subsequently, we dynamically adjust the contrastive decoding strategy based on the utility of retrieved contexts to enhance the utilization of relevant contexts while suppressing interference from noisy contexts. Extensive experiments on four medical question-answering datasets demonstrate that D²-RAG significantly outperforms baselines, enabling retrieval-augmented Llama3.1-8B to surpass non-retrieval-augmented Llama3.1-70B on the MedMCQA dataset. The source code is available on https://github.com/zakelawen/d–rag.

With open-source projects growing in size and complexity, manual compilation becomes tedious and error-prone, highlighting the need for automation to improve efficiency and accuracy. However, the complexity of compilation instruction search and error resolution makes automatic compilation challenging. Inspired by the success of LLM-based agents in various fields, we propose CompileAgent, the first LLM-based agent framework dedicated to repo-level compilation. CompileAgent integrates five tools and a flow-based agent strategy, enabling interaction with software artifacts for compilation instruction search and error resolution. To measure the effectiveness of our method, we design a public repo-level benchmark CompileAgentBench, and we also design two baselines for comparison by combining two compilation-friendly schemes. The performance on this benchmark shows that our method significantly improves the compilation success rate, ranging from 10% to 71%. Meanwhile, we evaluate the performance of CompileAgent under different agent strategies and verify the effectiveness of the flow-based strategy. Additionally, we emphasize the scalability of CompileAgent, further expanding its application prospects. The complete code and data are available at https://github.com/Ch3nYe/AutoCompiler.

"中文电子病历国际疾病分类(ICD)诊断编码评测依托第二十四届中国计算语言学大会(CCL)举办。该评测聚焦于自然语言处理技术在智能医疗领域的应用,旨在从真实脱敏的电子病历文本中自动分析关键临床表征,实现主诊断及其他诊断ICD编码的精准预测与分配,从而辅助临床医生与专业编码员提升编码工作的准确性和效率。本次评测在阿里云天池平台进行,获得了学术界与工业界的广泛关注和积极参与。数据显示,共有445支队伍报名参赛,其中A榜和B榜分别有85支和36支队伍成功提交了有效结果。最终,8支表现优异的队伍受邀撰写并分享了其技术报告,为推动该领域的技术进步与方法创新贡献了宝贵经验。本次评测的详细信息可参见相关发布页面。"